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              <p class="caption" role="heading"><span class="caption-text">Getting Started</span></p>
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<p class="caption" role="heading"><span class="caption-text">Tutorials</span></p>
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<li class="toctree-l1"><a class="reference internal" href="../../../../tutorials/_rendered_examples/dynamo/torch_compile_advanced_usage.html">Torch Compile Advanced Usage</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../../../../tutorials/_rendered_examples/dynamo/engine_caching_example.html">Engine Caching</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../../../../tutorials/_rendered_examples/dynamo/custom_kernel_plugins.html">Using Custom Kernels within TensorRT Engines with Torch-TensorRT</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../../../../dynamo/dynamo_export.html">Compiling Exported Programs with Torch-TensorRT</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../../../../ts/creating_torchscript_module_in_python.html">Creating a TorchScript Module</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../../ts/creating_torchscript_module_in_python.html#working-with-torchscript-in-python">Working with TorchScript in Python</a></li>
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<p class="caption" role="heading"><span class="caption-text">Model Zoo</span></p>
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<li class="toctree-l1"><a class="reference internal" href="../../../../tutorials/_rendered_examples/dynamo/torch_compile_resnet_example.html">Compiling ResNet with dynamic shapes using the <cite>torch.compile</cite> backend</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../../../../tutorials/compile_hf_models.html">Compiling LLM models from Huggingface</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../../tutorials/_rendered_examples/dynamo/torch_compile_gpt2.html">Compiling GPT2 using the Torch-TensorRT <code class="docutils literal notranslate"><span class="pre">torch.compile</span></code> frontend</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../../tutorials/_rendered_examples/dynamo/torch_export_sam2.html">Compiling SAM2 using the dynamo backend</a></li>
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<p class="caption" role="heading"><span class="caption-text">Python API Documentation</span></p>
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<p class="caption" role="heading"><span class="caption-text">C++ API Documentation</span></p>
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<li class="toctree-l1"><a class="reference internal" href="../../../../_cpp_api/namespace_torch_tensorrt__torchscript.html">Namespace torch_tensorrt::torchscript</a></li>
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<p class="caption" role="heading"><span class="caption-text">CLI Documentation</span></p>
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<p class="caption" role="heading"><span class="caption-text">Contributor Documentation</span></p>
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<li class="toctree-l1"><a class="reference internal" href="../../../../contributors/system_overview.html">System Overview</a></li>
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  <h1>Source code for torch_tensorrt.dynamo.runtime._PythonTorchTensorRTModule</h1><div class="highlight"><pre>
<span></span><span class="kn">from</span><span class="w"> </span><span class="nn">__future__</span><span class="w"> </span><span class="kn">import</span> <span class="n">annotations</span>

<span class="kn">import</span><span class="w"> </span><span class="nn">logging</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">contextlib</span><span class="w"> </span><span class="kn">import</span> <span class="n">nullcontext</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">typing</span><span class="w"> </span><span class="kn">import</span> <span class="n">Any</span><span class="p">,</span> <span class="n">Dict</span><span class="p">,</span> <span class="n">List</span><span class="p">,</span> <span class="n">Optional</span><span class="p">,</span> <span class="n">Sequence</span><span class="p">,</span> <span class="n">Tuple</span>

<span class="kn">import</span><span class="w"> </span><span class="nn">tensorrt</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">trt</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">torch</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">torch_tensorrt</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">torch.nn</span><span class="w"> </span><span class="kn">import</span> <span class="n">Module</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">torch_tensorrt._Device</span><span class="w"> </span><span class="kn">import</span> <span class="n">Device</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">torch_tensorrt._enums</span><span class="w"> </span><span class="kn">import</span> <span class="n">Platform</span><span class="p">,</span> <span class="n">dtype</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">torch_tensorrt.dynamo._defaults</span><span class="w"> </span><span class="kn">import</span> <span class="n">DEBUG_LOGGING_DIR</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">torch_tensorrt.dynamo._settings</span><span class="w"> </span><span class="kn">import</span> <span class="n">CompilationSettings</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">torch_tensorrt.dynamo.debug._DebuggerConfig</span><span class="w"> </span><span class="kn">import</span> <span class="n">DebuggerConfig</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">torch_tensorrt.dynamo.debug._supports_debugger</span><span class="w"> </span><span class="kn">import</span> <span class="n">cls_supports_debugger</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">torch_tensorrt.dynamo.utils</span><span class="w"> </span><span class="kn">import</span> <span class="n">DYNAMIC_DIM</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">torch_tensorrt.logging</span><span class="w"> </span><span class="kn">import</span> <span class="n">TRT_LOGGER</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">torch_tensorrt.runtime._utils</span><span class="w"> </span><span class="kn">import</span> <span class="p">(</span>
    <span class="n">_is_switch_required</span><span class="p">,</span>
    <span class="n">_select_rt_device</span><span class="p">,</span>
    <span class="n">multi_gpu_device_check</span><span class="p">,</span>
<span class="p">)</span>

<span class="n">logger</span> <span class="o">=</span> <span class="n">logging</span><span class="o">.</span><span class="n">getLogger</span><span class="p">(</span><span class="vm">__name__</span><span class="p">)</span>


<span class="k">class</span><span class="w"> </span><span class="nc">DynamicOutputAllocator</span><span class="p">(</span><span class="n">trt</span><span class="o">.</span><span class="n">IOutputAllocator</span><span class="p">):</span>  <span class="c1"># type: ignore[misc]</span>
    <span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">output_dtypes</span><span class="p">:</span> <span class="n">Dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">dtype</span><span class="p">])</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
        <span class="n">trt</span><span class="o">.</span><span class="n">IOutputAllocator</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">buffers</span><span class="p">:</span> <span class="n">Dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">]</span> <span class="o">=</span> <span class="p">{}</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">shapes</span><span class="p">:</span> <span class="n">Dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">Tuple</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="o">...</span><span class="p">]]</span> <span class="o">=</span> <span class="p">{}</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">dtypes</span><span class="p">:</span> <span class="n">Dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">dtype</span><span class="p">]</span> <span class="o">=</span> <span class="n">output_dtypes</span>

    <span class="k">def</span><span class="w"> </span><span class="nf">reallocate_output_async</span><span class="p">(</span>
        <span class="bp">self</span><span class="p">,</span>
        <span class="n">tensor_name</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span>
        <span class="n">memory</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>
        <span class="n">size</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>
        <span class="n">alignment</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>
        <span class="n">stream</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">Stream</span><span class="p">,</span>
    <span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Any</span><span class="p">:</span>
        <span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="n">size</span><span class="p">,)</span>
        <span class="k">if</span> <span class="n">tensor_name</span> <span class="ow">not</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">buffers</span><span class="p">:</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">buffers</span><span class="p">[</span><span class="n">tensor_name</span><span class="p">]</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span>
                <span class="n">shape</span><span class="p">,</span>
                <span class="n">dtype</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">dtypes</span><span class="p">[</span><span class="n">tensor_name</span><span class="p">],</span>
                <span class="n">device</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">current_device</span><span class="p">(),</span>
            <span class="p">)</span>
        <span class="k">else</span><span class="p">:</span>
            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">buffers</span><span class="p">[</span><span class="n">tensor_name</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span> <span class="o">!=</span> <span class="n">shape</span><span class="p">:</span>
                <span class="bp">self</span><span class="o">.</span><span class="n">buffers</span><span class="p">[</span><span class="n">tensor_name</span><span class="p">]</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span>
                    <span class="n">shape</span><span class="p">,</span>
                    <span class="n">dtype</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">dtypes</span><span class="p">[</span><span class="n">tensor_name</span><span class="p">],</span>
                    <span class="n">device</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">current_device</span><span class="p">(),</span>
                <span class="p">)</span>
        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">buffers</span><span class="p">[</span><span class="n">tensor_name</span><span class="p">]</span><span class="o">.</span><span class="n">data_ptr</span><span class="p">()</span>

    <span class="k">def</span><span class="w"> </span><span class="nf">notify_shape</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">tensor_name</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> <span class="n">shape</span><span class="p">:</span> <span class="n">Tuple</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="o">...</span><span class="p">])</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">shapes</span><span class="p">[</span><span class="n">tensor_name</span><span class="p">]</span> <span class="o">=</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">shape</span><span class="p">)</span>


<span class="k">class</span><span class="w"> </span><span class="nc">TorchTRTRuntimeStates</span><span class="p">:</span>
    <span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">new_cudagraphs</span><span class="p">:</span> <span class="nb">bool</span><span class="p">):</span>
        <span class="c1"># Indicates whether CUDAGraphs were enabled in the previous execute_engine</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">old_cudagraphs</span> <span class="o">=</span> <span class="n">new_cudagraphs</span>
        <span class="c1"># Indicates whether pre-allocated output was enabled in the previous execute_engine</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">old_pre_allocated_outputs</span> <span class="o">=</span> <span class="kc">False</span>
        <span class="c1"># Indicates whether context has changed</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">context_changed</span> <span class="o">=</span> <span class="kc">False</span>

    <span class="k">def</span><span class="w"> </span><span class="nf">set_runtime_states</span><span class="p">(</span>
        <span class="bp">self</span><span class="p">,</span>
        <span class="n">new_cudagraphs</span><span class="p">:</span> <span class="nb">bool</span><span class="p">,</span>
        <span class="n">new_pre_allocated_output</span><span class="p">:</span> <span class="nb">bool</span><span class="p">,</span>
        <span class="n">shape_changed</span><span class="p">:</span> <span class="nb">bool</span><span class="p">,</span>
    <span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Tuple</span><span class="p">[</span><span class="nb">bool</span><span class="p">,</span> <span class="nb">bool</span><span class="p">,</span> <span class="nb">bool</span><span class="p">]:</span>
        <span class="c1"># Evaluates whether certain conditions are met to enable CUDA Graph recording or to use pre-allocated outputs</span>
        <span class="c1"># based on the current and previous states, as well as input shape has changed</span>
        <span class="n">need_cudagraphs_record</span> <span class="o">=</span> <span class="kc">False</span>
        <span class="n">can_use_pre_allocated_outputs</span> <span class="o">=</span> <span class="kc">False</span>
        <span class="n">need_cudagraphs_reset</span> <span class="o">=</span> <span class="kc">False</span>

        <span class="c1"># CUDA Graph recording is needed if CUDA graphs is enabled and:</span>
        <span class="c1"># - CUDA graphs were previously disabled</span>
        <span class="c1"># - or the shape has changed</span>
        <span class="c1"># - or the execution context has changed (e.g., weight streaming)</span>
        <span class="k">if</span> <span class="n">new_cudagraphs</span> <span class="ow">and</span> <span class="p">(</span>
            <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">old_cudagraphs</span> <span class="ow">or</span> <span class="n">shape_changed</span> <span class="ow">or</span> <span class="bp">self</span><span class="o">.</span><span class="n">context_changed</span>
        <span class="p">):</span>
            <span class="n">need_cudagraphs_record</span> <span class="o">=</span> <span class="kc">True</span>

        <span class="c1"># Pre-allocated output can be used when previous and current state are true without shape change</span>
        <span class="k">if</span> <span class="p">(</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">old_pre_allocated_outputs</span>
            <span class="ow">and</span> <span class="n">new_pre_allocated_output</span>
            <span class="ow">and</span> <span class="p">(</span><span class="ow">not</span> <span class="n">shape_changed</span><span class="p">)</span>
        <span class="p">):</span>
            <span class="n">can_use_pre_allocated_outputs</span> <span class="o">=</span> <span class="kc">True</span>

        <span class="k">if</span> <span class="ow">not</span> <span class="n">new_cudagraphs</span> <span class="ow">or</span> <span class="n">shape_changed</span> <span class="ow">or</span> <span class="bp">self</span><span class="o">.</span><span class="n">context_changed</span><span class="p">:</span>
            <span class="n">need_cudagraphs_reset</span> <span class="o">=</span> <span class="kc">True</span>

        <span class="bp">self</span><span class="o">.</span><span class="n">old_cudagraphs</span> <span class="o">=</span> <span class="n">new_cudagraphs</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">old_pre_allocated_outputs</span> <span class="o">=</span> <span class="n">new_pre_allocated_output</span>
        <span class="c1"># reset flag</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">context_changed</span> <span class="o">=</span> <span class="kc">False</span>

        <span class="k">return</span> <span class="p">(</span>
            <span class="n">need_cudagraphs_record</span><span class="p">,</span>
            <span class="n">can_use_pre_allocated_outputs</span><span class="p">,</span>
            <span class="n">need_cudagraphs_reset</span><span class="p">,</span>
        <span class="p">)</span>


<div class="viewcode-block" id="PythonTorchTensorRTModule"><a class="viewcode-back" href="../../../../py_api/runtime.html#torch_tensorrt.runtime.PythonTorchTensorRTModule">[docs]</a><span class="nd">@cls_supports_debugger</span>
<span class="k">class</span><span class="w"> </span><span class="nc">PythonTorchTensorRTModule</span><span class="p">(</span><span class="n">Module</span><span class="p">):</span>  <span class="c1"># type: ignore[misc]</span>
<span class="w">    </span><span class="sd">&quot;&quot;&quot;PythonTorchTensorRTModule is a PyTorch module which encompasses an arbitrary TensorRT Engine.</span>

<span class="sd">    This module is backed by the Torch-TensorRT runtime and is only compatible with</span>
<span class="sd">    FX / Dynamo / Python deployments. This module cannot be serialized to torchscript via torch.jit.trace for C++ deployment.</span>
<span class="sd">    &quot;&quot;&quot;</span>

<div class="viewcode-block" id="PythonTorchTensorRTModule.__init__"><a class="viewcode-back" href="../../../../py_api/runtime.html#torch_tensorrt.runtime.PythonTorchTensorRTModule.__init__">[docs]</a>    <span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span>
        <span class="bp">self</span><span class="p">,</span>
        <span class="n">serialized_engine</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">bytes</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
        <span class="n">input_binding_names</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">List</span><span class="p">[</span><span class="nb">str</span><span class="p">]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
        <span class="n">output_binding_names</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">List</span><span class="p">[</span><span class="nb">str</span><span class="p">]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
        <span class="o">*</span><span class="p">,</span>
        <span class="n">name</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;&quot;</span><span class="p">,</span>
        <span class="n">settings</span><span class="p">:</span> <span class="n">CompilationSettings</span> <span class="o">=</span> <span class="n">CompilationSettings</span><span class="p">(),</span>
        <span class="n">weight_name_map</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">dict</span><span class="p">[</span><span class="n">Any</span><span class="p">,</span> <span class="n">Any</span><span class="p">]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
        <span class="n">requires_output_allocator</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
        <span class="n">_debugger_config</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">DebuggerConfig</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
    <span class="p">):</span>
<span class="w">        </span><span class="sd">&quot;&quot;&quot;Takes a name, target device, serialized TensorRT engine, and binding names / order and constructs</span>
<span class="sd">        a PyTorch ``torch.nn.Module`` around it. Uses TensorRT Python APIs to run the engine</span>

<span class="sd">        Arguments:</span>
<span class="sd">            serialized_engine (bytes): Serialized TensorRT engine in the form of a bytearray</span>
<span class="sd">            input_binding_names (List[str]): List of input TensorRT engine binding names in the order they would be passed to the TRT modules</span>
<span class="sd">            output_binding_names (List[str]): List of output TensorRT engine binding names in the order they should be returned</span>

<span class="sd">        Keyword Arguments:</span>
<span class="sd">            name (str): Name for module</span>
<span class="sd">            settings (torch_tensorrt.dynamo.CompilationSettings): Settings used to compile engine, assumes engine was built with default compilation settings if object not passed</span>
<span class="sd">            weight_name_map (dict): Mapping of engine weight name to state_dict weight name</span>
<span class="sd">            requires_output_allocator (bool): Boolean flag indicating if the converter creates operators which require an Output Allocator to run (e.g. data dependent operators)</span>

<span class="sd">        Example:</span>

<span class="sd">            .. code-block:: py</span>

<span class="sd">                trt_module = PythonTorchTensorRTModule(</span>
<span class="sd">                    engine_str,</span>
<span class="sd">                    input_binding_names=[&quot;x&quot;],</span>
<span class="sd">                    output_binding_names=[&quot;output&quot;],</span>
<span class="sd">                    name=&quot;my_module&quot;,</span>
<span class="sd">                    settings=CompilationSettings(device=torch.cuda.current_device)</span>
<span class="sd">                )</span>

<span class="sd">        &quot;&quot;&quot;</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">context</span><span class="p">:</span> <span class="n">Any</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">_debugger_config</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">DebuggerConfig</span><span class="p">]</span> <span class="o">=</span> <span class="n">_debugger_config</span>
        <span class="nb">super</span><span class="p">(</span><span class="n">PythonTorchTensorRTModule</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">_register_state_dict_hook</span><span class="p">(</span><span class="n">PythonTorchTensorRTModule</span><span class="o">.</span><span class="n">_on_state_dict</span><span class="p">)</span>

        <span class="c1"># Run multi-gpu device check to validate engine instantiation</span>
        <span class="n">multi_gpu_device_check</span><span class="p">()</span>

        <span class="bp">self</span><span class="o">.</span><span class="n">name</span> <span class="o">=</span> <span class="n">name</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">_input_buffers</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">]</span> <span class="o">=</span> <span class="p">[]</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">_output_buffers</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">]</span> <span class="o">=</span> <span class="p">[]</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">cudagraph</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">CUDAGraph</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">_caller_stream</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">Stream</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">_engine_stream</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">Stream</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span>

        <span class="c1"># TODO: Make the below a Dictionary {shape: cudagraph}</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">shape_key</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span>

        <span class="c1"># See https://github.com/pytorch/pytorch/blob/acfe237a71af609e837a34bb38048aa8acb8eb4d/torch/cuda/graphs.py#L92-L98</span>
        <span class="c1"># Unused currently - to be used by Dynamic Shape support implementation</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">memory_pool</span> <span class="o">=</span> <span class="kc">None</span>

        <span class="bp">self</span><span class="o">.</span><span class="n">serialized_engine</span> <span class="o">=</span> <span class="n">serialized_engine</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">input_names</span> <span class="o">=</span> <span class="p">(</span>
            <span class="n">input_binding_names</span> <span class="k">if</span> <span class="n">input_binding_names</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="k">else</span> <span class="p">[]</span>
        <span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">output_names</span> <span class="o">=</span> <span class="p">(</span>
            <span class="n">output_binding_names</span> <span class="k">if</span> <span class="n">output_binding_names</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="k">else</span> <span class="p">[]</span>
        <span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">initialized</span> <span class="o">=</span> <span class="kc">False</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">target_device_id</span> <span class="o">=</span> <span class="p">(</span>
            <span class="n">settings</span><span class="o">.</span><span class="n">device</span><span class="o">.</span><span class="n">gpu_id</span>
            <span class="k">if</span> <span class="n">settings</span><span class="o">.</span><span class="n">device</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span>
            <span class="k">else</span> <span class="n">Device</span><span class="o">.</span><span class="n">_current_device</span><span class="p">()</span><span class="o">.</span><span class="n">gpu_id</span>
        <span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">target_device_properties</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">get_device_properties</span><span class="p">(</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">target_device_id</span>
        <span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">profiling_enabled</span> <span class="o">=</span> <span class="p">(</span>
            <span class="n">_debugger_config</span><span class="o">.</span><span class="n">save_engine_profile</span>
            <span class="k">if</span> <span class="n">_debugger_config</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span>
            <span class="k">else</span> <span class="kc">False</span>
        <span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">settings</span> <span class="o">=</span> <span class="n">settings</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">engine</span> <span class="o">=</span> <span class="kc">None</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">weight_name_map</span> <span class="o">=</span> <span class="n">weight_name_map</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">target_platform</span> <span class="o">=</span> <span class="n">Platform</span><span class="o">.</span><span class="n">current_platform</span><span class="p">()</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">runtime_states</span> <span class="o">=</span> <span class="n">TorchTRTRuntimeStates</span><span class="p">(</span>
            <span class="n">torch_tensorrt</span><span class="o">.</span><span class="n">runtime</span><span class="o">.</span><span class="n">get_cudagraphs_mode</span><span class="p">()</span>
        <span class="p">)</span>

        <span class="bp">self</span><span class="o">.</span><span class="n">cudagraphs_enabled</span> <span class="o">=</span> <span class="kc">False</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">pre_allocated_outputs</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">]</span> <span class="o">=</span> <span class="p">[]</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">use_pre_allocated_outputs</span> <span class="o">=</span> <span class="kc">False</span>

        <span class="bp">self</span><span class="o">.</span><span class="n">requires_output_allocator</span> <span class="o">=</span> <span class="n">requires_output_allocator</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">output_allocator</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">DynamicOutputAllocator</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">use_output_allocator_outputs</span> <span class="o">=</span> <span class="kc">False</span>

        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">serialized_engine</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">settings</span><span class="o">.</span><span class="n">lazy_engine_init</span><span class="p">:</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">setup_engine</span><span class="p">()</span></div>

    <span class="k">def</span><span class="w"> </span><span class="nf">get_streamable_device_memory_budget</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Any</span><span class="p">:</span>
        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">engine</span><span class="o">.</span><span class="n">streamable_weights_size</span>

    <span class="k">def</span><span class="w"> </span><span class="nf">get_automatic_device_memory_budget</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Any</span><span class="p">:</span>
        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">engine</span><span class="o">.</span><span class="n">get_weight_streaming_automatic_budget</span><span class="p">()</span>

    <span class="k">def</span><span class="w"> </span><span class="nf">get_device_memory_budget</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Any</span><span class="p">:</span>
        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">engine</span><span class="o">.</span><span class="n">weight_streaming_budget_v2</span>

    <span class="k">def</span><span class="w"> </span><span class="nf">set_device_memory_budget</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">budget_bytes</span><span class="p">:</span> <span class="nb">int</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">int</span><span class="p">:</span>
        <span class="c1"># Recreating the context because weight streaming budget cannot be modified while there are active context.</span>
        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">context</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
            <span class="k">del</span> <span class="bp">self</span><span class="o">.</span><span class="n">context</span>
        <span class="n">budget_bytes</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_set_device_memory_budget</span><span class="p">(</span><span class="n">budget_bytes</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">context</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">engine</span><span class="o">.</span><span class="n">create_execution_context</span><span class="p">()</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">runtime_states</span><span class="o">.</span><span class="n">context_changed</span> <span class="o">=</span> <span class="kc">True</span>
        <span class="k">return</span> <span class="n">budget_bytes</span>

    <span class="k">def</span><span class="w"> </span><span class="nf">_set_device_memory_budget</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">budget_bytes</span><span class="p">:</span> <span class="nb">int</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">int</span><span class="p">:</span>
        <span class="c1"># Disable weight streaming for invalid budget size</span>
        <span class="k">if</span> <span class="n">budget_bytes</span> <span class="o">&lt;</span> <span class="mi">0</span><span class="p">:</span>
            <span class="n">budget_bytes</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_streamable_device_memory_budget</span><span class="p">()</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">engine</span><span class="o">.</span><span class="n">weight_streaming_budget_v2</span> <span class="o">=</span> <span class="n">budget_bytes</span>
        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">engine</span><span class="o">.</span><span class="n">weight_streaming_budget_v2</span> <span class="o">!=</span> <span class="n">budget_bytes</span><span class="p">:</span>
            <span class="n">logger</span><span class="o">.</span><span class="n">error</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Failed to set weight streaming budget to </span><span class="si">{</span><span class="n">budget_bytes</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
            <span class="n">budget_bytes</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">engine</span><span class="o">.</span><span class="n">weight_streaming_budget_v2</span>
        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_streamable_device_memory_budget</span><span class="p">()</span> <span class="o">==</span> <span class="n">budget_bytes</span><span class="p">:</span>
            <span class="n">logger</span><span class="o">.</span><span class="n">warning</span><span class="p">(</span><span class="s2">&quot;Weight streaming is disabled&quot;</span><span class="p">)</span>

        <span class="k">return</span> <span class="n">budget_bytes</span>

    <span class="k">def</span><span class="w"> </span><span class="nf">set_default_device_memory_budget</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">int</span><span class="p">:</span>
        <span class="n">budget_bytes</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_automatic_device_memory_budget</span><span class="p">()</span>
        <span class="c1"># Set automatic weight streaming budget as default when context is created</span>
        <span class="n">logger</span><span class="o">.</span><span class="n">debug</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Weight streaming budget set to </span><span class="si">{</span><span class="n">budget_bytes</span><span class="si">}</span><span class="s2">B&quot;</span><span class="p">)</span>
        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_set_device_memory_budget</span><span class="p">(</span><span class="n">budget_bytes</span><span class="p">)</span>

    <span class="k">def</span><span class="w"> </span><span class="nf">setup_engine</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
        <span class="k">assert</span> <span class="p">(</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">target_platform</span> <span class="o">==</span> <span class="n">Platform</span><span class="o">.</span><span class="n">current_platform</span><span class="p">()</span>
        <span class="p">),</span> <span class="sa">f</span><span class="s2">&quot;TensorRT engine was not built to target current platform (target: </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">target_platform</span><span class="si">}</span><span class="s2">, current: </span><span class="si">{</span><span class="n">Platform</span><span class="o">.</span><span class="n">current_platform</span><span class="p">()</span><span class="si">}</span><span class="s2">)&quot;</span>

        <span class="bp">self</span><span class="o">.</span><span class="n">initialized</span> <span class="o">=</span> <span class="kc">True</span>
        <span class="n">runtime</span> <span class="o">=</span> <span class="n">trt</span><span class="o">.</span><span class="n">Runtime</span><span class="p">(</span><span class="n">TRT_LOGGER</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">engine</span> <span class="o">=</span> <span class="n">runtime</span><span class="o">.</span><span class="n">deserialize_cuda_engine</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">serialized_engine</span><span class="p">)</span>
        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">settings</span><span class="o">.</span><span class="n">enable_weight_streaming</span><span class="p">:</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">set_default_device_memory_budget</span><span class="p">()</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">context</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">engine</span><span class="o">.</span><span class="n">create_execution_context</span><span class="p">()</span>
        <span class="k">assert</span> <span class="bp">self</span><span class="o">.</span><span class="n">context</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">,</span> <span class="s2">&quot;Failed to create execution context&quot;</span>
        <span class="k">assert</span> <span class="bp">self</span><span class="o">.</span><span class="n">engine</span><span class="o">.</span><span class="n">num_io_tensors</span> <span class="o">==</span> <span class="p">(</span>
            <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">input_names</span><span class="p">)</span> <span class="o">+</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">output_names</span><span class="p">)</span>
        <span class="p">)</span>

        <span class="bp">self</span><span class="o">.</span><span class="n">input_dtypes</span> <span class="o">=</span> <span class="p">[</span>
            <span class="n">dtype</span><span class="o">.</span><span class="n">_from</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">engine</span><span class="o">.</span><span class="n">get_tensor_dtype</span><span class="p">(</span><span class="n">input_name</span><span class="p">))</span>
            <span class="k">for</span> <span class="n">input_name</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">input_names</span>
        <span class="p">]</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">input_shapes</span> <span class="o">=</span> <span class="p">[</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">engine</span><span class="o">.</span><span class="n">get_tensor_shape</span><span class="p">(</span><span class="n">input_name</span><span class="p">)</span> <span class="k">for</span> <span class="n">input_name</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">input_names</span>
        <span class="p">]</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">output_dtypes</span> <span class="o">=</span> <span class="p">[</span>
            <span class="n">dtype</span><span class="o">.</span><span class="n">_from</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">engine</span><span class="o">.</span><span class="n">get_tensor_dtype</span><span class="p">(</span><span class="n">output_name</span><span class="p">))</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span>
            <span class="k">for</span> <span class="n">output_name</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">output_names</span>
        <span class="p">]</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">output_shapes</span> <span class="o">=</span> <span class="p">[</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">engine</span><span class="o">.</span><span class="n">get_tensor_shape</span><span class="p">(</span><span class="n">output_name</span><span class="p">)</span>
            <span class="k">for</span> <span class="n">output_name</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">output_names</span>
        <span class="p">]</span>

        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">requires_output_allocator</span><span class="p">:</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">create_output_allocator</span><span class="p">()</span>

        <span class="k">if</span> <span class="n">torch_tensorrt</span><span class="o">.</span><span class="n">runtime</span><span class="o">.</span><span class="n">get_cudagraphs_mode</span><span class="p">():</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">cudagraph</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">CUDAGraph</span><span class="p">()</span>

    <span class="k">def</span><span class="w"> </span><span class="nf">_check_initialized</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
        <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">initialized</span><span class="p">:</span>
            <span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span><span class="s2">&quot;PythonTorchTensorRTModule is not initialized.&quot;</span><span class="p">)</span>

    <span class="k">def</span><span class="w"> </span><span class="nf">_on_state_dict</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">state_dict</span><span class="p">:</span> <span class="n">Dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">Any</span><span class="p">],</span> <span class="n">prefix</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> <span class="n">_</span><span class="p">:</span> <span class="n">Any</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
        <span class="n">state_dict</span><span class="p">[</span><span class="n">prefix</span> <span class="o">+</span> <span class="s2">&quot;engine&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">serialized_engine</span>
        <span class="n">state_dict</span><span class="p">[</span><span class="n">prefix</span> <span class="o">+</span> <span class="s2">&quot;input_names&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">input_names</span>
        <span class="n">state_dict</span><span class="p">[</span><span class="n">prefix</span> <span class="o">+</span> <span class="s2">&quot;output_names&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">output_names</span>
        <span class="n">state_dict</span><span class="p">[</span><span class="n">prefix</span> <span class="o">+</span> <span class="s2">&quot;platform&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">target_platform</span>

    <span class="k">def</span><span class="w"> </span><span class="nf">_load_from_state_dict</span><span class="p">(</span>
        <span class="bp">self</span><span class="p">,</span>
        <span class="n">state_dict</span><span class="p">:</span> <span class="n">Dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">Any</span><span class="p">],</span>
        <span class="n">prefix</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span>
        <span class="n">local_metadata</span><span class="p">:</span> <span class="n">Any</span><span class="p">,</span>
        <span class="n">strict</span><span class="p">:</span> <span class="n">Any</span><span class="p">,</span>
        <span class="n">missing_keys</span><span class="p">:</span> <span class="n">Any</span><span class="p">,</span>
        <span class="n">unexpected_keys</span><span class="p">:</span> <span class="n">Any</span><span class="p">,</span>
        <span class="n">error_msgs</span><span class="p">:</span> <span class="n">Any</span><span class="p">,</span>
    <span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">serialized_engine</span> <span class="o">=</span> <span class="n">state_dict</span><span class="p">[</span><span class="n">prefix</span> <span class="o">+</span> <span class="s2">&quot;engine&quot;</span><span class="p">]</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">input_names</span> <span class="o">=</span> <span class="n">state_dict</span><span class="p">[</span><span class="n">prefix</span> <span class="o">+</span> <span class="s2">&quot;input_names&quot;</span><span class="p">]</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">output_names</span> <span class="o">=</span> <span class="n">state_dict</span><span class="p">[</span><span class="n">prefix</span> <span class="o">+</span> <span class="s2">&quot;output_names&quot;</span><span class="p">]</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">target_platform</span> <span class="o">=</span> <span class="n">state_dict</span><span class="p">[</span><span class="n">prefix</span> <span class="o">+</span> <span class="s2">&quot;platform&quot;</span><span class="p">]</span>

        <span class="c1"># Run multi-gpu device check to validate engine instantiation</span>
        <span class="n">multi_gpu_device_check</span><span class="p">()</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">setup_engine</span><span class="p">()</span>

    <span class="k">def</span><span class="w"> </span><span class="nf">__getstate__</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">Any</span><span class="p">]:</span>
        <span class="n">state</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="vm">__dict__</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
        <span class="n">state</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s2">&quot;engine&quot;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
        <span class="n">state</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s2">&quot;context&quot;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
        <span class="k">return</span> <span class="n">state</span>

    <span class="k">def</span><span class="w"> </span><span class="nf">__setstate__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">state</span><span class="p">:</span> <span class="n">Dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">Any</span><span class="p">])</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
        <span class="bp">self</span><span class="o">.</span><span class="vm">__dict__</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">state</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">setup_engine</span><span class="p">()</span>

    <span class="k">def</span><span class="w"> </span><span class="nf">__deepcopy__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">memo</span><span class="p">:</span> <span class="n">Any</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">PythonTorchTensorRTModule</span><span class="p">:</span>
        <span class="bp">cls</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span>
        <span class="n">result</span> <span class="o">=</span> <span class="bp">cls</span><span class="o">.</span><span class="fm">__new__</span><span class="p">(</span><span class="bp">cls</span><span class="p">)</span>
        <span class="n">memo</span><span class="p">[</span><span class="nb">id</span><span class="p">(</span><span class="bp">self</span><span class="p">)]</span> <span class="o">=</span> <span class="n">result</span>
        <span class="n">result</span><span class="o">.</span><span class="n">__setstate__</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">__getstate__</span><span class="p">())</span>
        <span class="k">return</span> <span class="n">result</span>

    <span class="k">def</span><span class="w"> </span><span class="nf">_reset_captured_graph</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">cudagraph</span><span class="p">:</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">cudagraph</span><span class="o">.</span><span class="n">reset</span><span class="p">()</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">cudagraph</span> <span class="o">=</span> <span class="kc">None</span>

    <span class="k">def</span><span class="w"> </span><span class="fm">__del__</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">_reset_captured_graph</span><span class="p">()</span>

    <span class="k">def</span><span class="w"> </span><span class="nf">setup_input_tensors</span><span class="p">(</span>
        <span class="bp">self</span><span class="p">,</span>
        <span class="n">contiguous_inputs</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">],</span>
        <span class="n">cudagraphs_enabled</span><span class="p">:</span> <span class="nb">bool</span><span class="p">,</span>
        <span class="n">need_cudagraphs_record</span><span class="p">:</span> <span class="nb">bool</span><span class="p">,</span>
    <span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
        <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">input_name</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">input_names</span><span class="p">):</span>
            <span class="k">if</span> <span class="ow">not</span> <span class="n">contiguous_inputs</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">is_cuda</span><span class="p">:</span>
                <span class="n">logger</span><span class="o">.</span><span class="n">warning</span><span class="p">(</span>
                    <span class="sa">f</span><span class="s2">&quot;Detected input </span><span class="si">{</span><span class="n">input_name</span><span class="si">}</span><span class="s2"> of engine </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">engine</span><span class="o">.</span><span class="n">name</span><span class="si">}</span><span class="s2"> is not on a cuda device. &quot;</span>
                    <span class="s2">&quot;This tensor is being moved by the runtime but for performance considerations, &quot;</span>
                    <span class="s2">&quot;ensure your inputs are all on GPU and open an issue here &quot;</span>
                    <span class="s2">&quot;(https://github.com/pytorch/TensorRT/issues) if this warning persists.&quot;</span>
                <span class="p">)</span>
                <span class="n">contiguous_inputs</span> <span class="o">=</span> <span class="p">(</span>
                    <span class="n">contiguous_inputs</span><span class="p">[:</span><span class="n">i</span><span class="p">]</span>
                    <span class="o">+</span> <span class="p">[</span><span class="n">contiguous_inputs</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">cuda</span><span class="p">()]</span>
                    <span class="o">+</span> <span class="n">contiguous_inputs</span><span class="p">[</span><span class="n">i</span> <span class="o">+</span> <span class="mi">1</span> <span class="p">:]</span>
                <span class="p">)</span>

            <span class="k">assert</span> <span class="p">(</span>
                <span class="n">contiguous_inputs</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">dtype</span> <span class="o">==</span> <span class="bp">self</span><span class="o">.</span><span class="n">input_dtypes</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
            <span class="p">),</span> <span class="sa">f</span><span class="s2">&quot;Dtype mismatch for </span><span class="si">{</span><span class="n">i</span><span class="si">}</span><span class="s2">th input(</span><span class="si">{</span><span class="n">input_name</span><span class="si">}</span><span class="s2">). Expect </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">input_dtypes</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="si">}</span><span class="s2">, got </span><span class="si">{</span><span class="n">contiguous_inputs</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">dtype</span><span class="si">}</span><span class="s2">.&quot;</span>

            <span class="k">if</span> <span class="n">need_cudagraphs_record</span><span class="p">:</span>
                <span class="c1"># If cudagraphs is enabled, this memory is reserved for future cudagraph runs</span>
                <span class="c1"># Clone is required to avoid re-using user-provided GPU memory</span>
                <span class="bp">self</span><span class="o">.</span><span class="n">_input_buffers</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">contiguous_inputs</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">clone</span><span class="p">()</span>

            <span class="c1"># For shape tensors, we use CPU pointers and for data tensors, we use GPU pointers</span>
            <span class="c1"># as per TensorRT requirements</span>
            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">engine</span><span class="o">.</span><span class="n">is_shape_inference_io</span><span class="p">(</span><span class="n">input_name</span><span class="p">):</span>
                <span class="c1"># Shape tensor inputs are casted to int64 explicitly</span>
                <span class="c1"># Currently Torch CPU pointers are not working; numpy pointers are used instead</span>
                <span class="c1"># to refer to underlying memory</span>
                <span class="n">inputs_cpu</span> <span class="o">=</span> <span class="n">contiguous_inputs</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">cpu</span><span class="p">()</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">int64</span><span class="p">)</span><span class="o">.</span><span class="n">numpy</span><span class="p">()</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
                <span class="bp">self</span><span class="o">.</span><span class="n">context</span><span class="o">.</span><span class="n">set_tensor_address</span><span class="p">(</span><span class="n">input_name</span><span class="p">,</span> <span class="n">inputs_cpu</span><span class="o">.</span><span class="n">ctypes</span><span class="o">.</span><span class="n">data</span><span class="p">)</span>
            <span class="k">else</span><span class="p">:</span>
                <span class="bp">self</span><span class="o">.</span><span class="n">context</span><span class="o">.</span><span class="n">set_input_shape</span><span class="p">(</span>
                    <span class="n">input_name</span><span class="p">,</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">contiguous_inputs</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
                <span class="p">)</span>
                <span class="k">if</span> <span class="n">cudagraphs_enabled</span><span class="p">:</span>
                    <span class="bp">self</span><span class="o">.</span><span class="n">_input_buffers</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">copy_</span><span class="p">(</span><span class="n">contiguous_inputs</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
                    <span class="bp">self</span><span class="o">.</span><span class="n">context</span><span class="o">.</span><span class="n">set_tensor_address</span><span class="p">(</span>
                        <span class="n">input_name</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_input_buffers</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">data_ptr</span><span class="p">()</span>
                    <span class="p">)</span>
                <span class="k">else</span><span class="p">:</span>
                    <span class="bp">self</span><span class="o">.</span><span class="n">context</span><span class="o">.</span><span class="n">set_tensor_address</span><span class="p">(</span>
                        <span class="n">input_name</span><span class="p">,</span> <span class="n">contiguous_inputs</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">data_ptr</span><span class="p">()</span>
                    <span class="p">)</span>

    <span class="k">def</span><span class="w"> </span><span class="nf">create_output_tensors</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">List</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">]:</span>
        <span class="c1"># create output tensors</span>
        <span class="n">outputs</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">]</span> <span class="o">=</span> <span class="p">[]</span>

        <span class="k">for</span> <span class="n">o</span><span class="p">,</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">output_names</span><span class="p">):</span>
            <span class="n">output</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span>
                <span class="n">size</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">output_shapes</span><span class="p">[</span><span class="n">o</span><span class="p">],</span>
                <span class="n">dtype</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">output_dtypes</span><span class="p">[</span><span class="n">o</span><span class="p">],</span>
                <span class="n">device</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">current_device</span><span class="p">(),</span>
            <span class="p">)</span>
            <span class="n">outputs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">output</span><span class="p">)</span>
        <span class="k">return</span> <span class="n">outputs</span>

    <span class="k">def</span><span class="w"> </span><span class="nf">set_pre_allocated_outputs</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">enable</span><span class="p">:</span> <span class="nb">bool</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">use_pre_allocated_outputs</span> <span class="o">=</span> <span class="n">enable</span>

    <span class="k">def</span><span class="w"> </span><span class="nf">set_use_output_allocator</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">enable</span><span class="p">:</span> <span class="nb">bool</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">use_output_allocator_outputs</span> <span class="o">=</span> <span class="n">enable</span>

    <span class="k">def</span><span class="w"> </span><span class="nf">create_output_allocator</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">output_allocator</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
            <span class="n">output_dtypes_dict</span> <span class="o">=</span> <span class="p">{}</span>
            <span class="k">for</span> <span class="n">o</span><span class="p">,</span> <span class="n">output_name</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">output_names</span><span class="p">):</span>
                <span class="n">output_dtypes_dict</span><span class="p">[</span><span class="n">output_name</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">output_dtypes</span><span class="p">[</span><span class="n">o</span><span class="p">]</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">output_allocator</span> <span class="o">=</span> <span class="n">DynamicOutputAllocator</span><span class="p">(</span><span class="n">output_dtypes_dict</span><span class="p">)</span>

<div class="viewcode-block" id="PythonTorchTensorRTModule.forward"><a class="viewcode-back" href="../../../../py_api/runtime.html#torch_tensorrt.runtime.PythonTorchTensorRTModule.forward">[docs]</a>    <span class="k">def</span><span class="w"> </span><span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">*</span><span class="n">inputs</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span> <span class="o">|</span> <span class="n">Tuple</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="o">...</span><span class="p">]:</span>

        <span class="k">def</span><span class="w"> </span><span class="nf">run_standard_execution</span><span class="p">()</span> <span class="o">-&gt;</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span> <span class="o">|</span> <span class="n">Tuple</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="o">...</span><span class="p">]:</span>
            <span class="n">shape_changed</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">validate_input_shapes</span><span class="p">(</span><span class="n">contiguous_inputs</span><span class="p">)</span>
            <span class="p">(</span>
                <span class="n">need_cudagraphs_record</span><span class="p">,</span>
                <span class="n">can_use_pre_allocated_outputs</span><span class="p">,</span>
                <span class="n">need_cudagraphs_reset</span><span class="p">,</span>
            <span class="p">)</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">runtime_states</span><span class="o">.</span><span class="n">set_runtime_states</span><span class="p">(</span>
                <span class="bp">self</span><span class="o">.</span><span class="n">cudagraphs_enabled</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">use_pre_allocated_outputs</span><span class="p">,</span> <span class="n">shape_changed</span>
            <span class="p">)</span>

            <span class="k">if</span> <span class="n">need_cudagraphs_reset</span><span class="p">:</span>
                <span class="bp">self</span><span class="o">.</span><span class="n">_reset_captured_graph</span><span class="p">()</span>

            <span class="k">if</span> <span class="n">need_cudagraphs_record</span><span class="p">:</span>
                <span class="bp">self</span><span class="o">.</span><span class="n">_input_buffers</span> <span class="o">=</span> <span class="p">[</span><span class="kc">None</span><span class="p">]</span> <span class="o">*</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">input_names</span><span class="p">)</span>
                <span class="bp">self</span><span class="o">.</span><span class="n">_output_buffers</span> <span class="o">=</span> <span class="p">[</span><span class="kc">None</span><span class="p">]</span> <span class="o">*</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">output_names</span><span class="p">)</span>

            <span class="k">with</span> <span class="p">(</span>
                <span class="n">torch</span><span class="o">.</span><span class="n">autograd</span><span class="o">.</span><span class="n">profiler</span><span class="o">.</span><span class="n">record_function</span><span class="p">(</span>
                    <span class="s2">&quot;PythonTorchTensorRTModule:ProcessInputs&quot;</span>
                <span class="p">)</span>
                <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">profiling_enabled</span>
                <span class="k">else</span> <span class="n">nullcontext</span><span class="p">()</span>
            <span class="p">):</span>
                <span class="k">assert</span> <span class="nb">len</span><span class="p">(</span><span class="n">contiguous_inputs</span><span class="p">)</span> <span class="o">==</span> <span class="nb">len</span><span class="p">(</span>
                    <span class="bp">self</span><span class="o">.</span><span class="n">input_names</span>
                <span class="p">),</span> <span class="sa">f</span><span class="s2">&quot;Wrong number of inputs, expect </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">input_names</span><span class="p">)</span><span class="si">}</span><span class="s2"> get </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">contiguous_inputs</span><span class="p">)</span><span class="si">}</span><span class="s2">.&quot;</span>

                <span class="bp">self</span><span class="o">.</span><span class="n">setup_input_tensors</span><span class="p">(</span>
                    <span class="n">contiguous_inputs</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">cudagraphs_enabled</span><span class="p">,</span> <span class="n">need_cudagraphs_record</span>
                <span class="p">)</span>

                <span class="k">if</span> <span class="n">shape_changed</span><span class="p">:</span>
                    <span class="c1"># Check if input shapes can be inferred.</span>
                    <span class="n">uninferred_input_names</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">context</span><span class="o">.</span><span class="n">infer_shapes</span><span class="p">()</span>
                    <span class="k">if</span> <span class="n">uninferred_input_names</span><span class="p">:</span>
                        <span class="n">logger</span><span class="o">.</span><span class="n">warning</span><span class="p">(</span>
                            <span class="sa">f</span><span class="s2">&quot;The shapes of the inputs: </span><span class="si">{</span><span class="n">uninferred_input_names</span><span class="si">}</span><span class="s2"> cannot be inferred and could lead to undefined behavior. </span><span class="se">\</span>
<span class="s2">                                    This could happen if the input tensor addresses/shapes haven&#39;t been configured correctly&quot;</span>
                        <span class="p">)</span>

            <span class="k">with</span> <span class="p">(</span>
                <span class="n">torch</span><span class="o">.</span><span class="n">autograd</span><span class="o">.</span><span class="n">profiler</span><span class="o">.</span><span class="n">record_function</span><span class="p">(</span>
                    <span class="s2">&quot;PythonTorchTensorRTModule:ProcessOutputs&quot;</span>
                <span class="p">)</span>
                <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">profiling_enabled</span>
                <span class="k">else</span> <span class="n">nullcontext</span><span class="p">()</span>
            <span class="p">):</span>
                <span class="k">if</span> <span class="n">can_use_pre_allocated_outputs</span><span class="p">:</span>
                    <span class="n">outputs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">pre_allocated_outputs</span>
                <span class="k">else</span><span class="p">:</span>
                    <span class="bp">self</span><span class="o">.</span><span class="n">output_shapes</span> <span class="o">=</span> <span class="p">[</span>
                        <span class="nb">tuple</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">context</span><span class="o">.</span><span class="n">get_tensor_shape</span><span class="p">(</span><span class="n">output_name</span><span class="p">))</span>
                        <span class="k">for</span> <span class="n">output_name</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">output_names</span>
                    <span class="p">]</span>
                    <span class="k">if</span> <span class="n">DYNAMIC_DIM</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">output_shapes</span><span class="p">:</span>
                        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
                            <span class="s2">&quot;Encountered dynamic output shapes during runtime. This could mean the network has data-dependent output shapes which is not currently supported.&quot;</span>
                        <span class="p">)</span>
                    <span class="n">outputs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">create_output_tensors</span><span class="p">()</span>

                <span class="k">for</span> <span class="n">o</span><span class="p">,</span> <span class="n">output_name</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">output_names</span><span class="p">):</span>
                    <span class="k">if</span> <span class="n">need_cudagraphs_record</span><span class="p">:</span>
                        <span class="bp">self</span><span class="o">.</span><span class="n">_output_buffers</span><span class="p">[</span><span class="n">o</span><span class="p">]</span> <span class="o">=</span> <span class="n">outputs</span><span class="p">[</span><span class="n">o</span><span class="p">]</span><span class="o">.</span><span class="n">clone</span><span class="p">()</span>

                    <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">cudagraphs_enabled</span><span class="p">:</span>
                        <span class="bp">self</span><span class="o">.</span><span class="n">context</span><span class="o">.</span><span class="n">set_tensor_address</span><span class="p">(</span>
                            <span class="n">output_name</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_output_buffers</span><span class="p">[</span><span class="n">o</span><span class="p">]</span><span class="o">.</span><span class="n">data_ptr</span><span class="p">()</span>
                        <span class="p">)</span>
                    <span class="k">else</span><span class="p">:</span>
                        <span class="bp">self</span><span class="o">.</span><span class="n">context</span><span class="o">.</span><span class="n">set_tensor_address</span><span class="p">(</span>
                            <span class="n">output_name</span><span class="p">,</span> <span class="n">outputs</span><span class="p">[</span><span class="n">o</span><span class="p">]</span><span class="o">.</span><span class="n">data_ptr</span><span class="p">()</span>
                        <span class="p">)</span>

            <span class="k">with</span> <span class="p">(</span>
                <span class="n">torch</span><span class="o">.</span><span class="n">autograd</span><span class="o">.</span><span class="n">profiler</span><span class="o">.</span><span class="n">record_function</span><span class="p">(</span>
                    <span class="s2">&quot;PythonTorchTensorRTModule:TensorRTRuntime&quot;</span>
                <span class="p">)</span>
                <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">profiling_enabled</span>
                <span class="k">else</span> <span class="n">nullcontext</span><span class="p">()</span>
            <span class="p">):</span>
                <span class="bp">self</span><span class="o">.</span><span class="n">_caller_stream</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">current_stream</span><span class="p">()</span>
                <span class="k">if</span> <span class="p">(</span>
                    <span class="bp">self</span><span class="o">.</span><span class="n">_engine_stream</span> <span class="o">==</span> <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">default_stream</span><span class="p">()</span>
                    <span class="ow">or</span> <span class="bp">self</span><span class="o">.</span><span class="n">_engine_stream</span> <span class="ow">is</span> <span class="kc">None</span>
                <span class="p">):</span>
                    <span class="bp">self</span><span class="o">.</span><span class="n">_engine_stream</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">Stream</span><span class="p">()</span>

                <span class="bp">self</span><span class="o">.</span><span class="n">_engine_stream</span><span class="o">.</span><span class="n">wait_stream</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_caller_stream</span><span class="p">)</span>

                <span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">stream</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_engine_stream</span><span class="p">):</span>
                    <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">cudagraphs_enabled</span><span class="p">:</span>
                        <span class="k">if</span> <span class="n">need_cudagraphs_record</span><span class="p">:</span>
                            <span class="bp">self</span><span class="o">.</span><span class="n">cudagraph</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">CUDAGraph</span><span class="p">()</span>

                            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">profiling_enabled</span><span class="p">:</span>
                                <span class="bp">self</span><span class="o">.</span><span class="n">cudagraph</span><span class="o">.</span><span class="n">enable_debug_mode</span><span class="p">()</span>

                            <span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">graph</span><span class="p">(</span>
                                <span class="bp">self</span><span class="o">.</span><span class="n">cudagraph</span><span class="p">,</span> <span class="n">stream</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_engine_stream</span>
                            <span class="p">):</span>
                                <span class="bp">self</span><span class="o">.</span><span class="n">context</span><span class="o">.</span><span class="n">execute_async_v3</span><span class="p">(</span>
                                    <span class="bp">self</span><span class="o">.</span><span class="n">_engine_stream</span><span class="o">.</span><span class="n">cuda_stream</span>
                                <span class="p">)</span>

                            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">profiling_enabled</span><span class="p">:</span>
                                <span class="bp">self</span><span class="o">.</span><span class="n">cudagraph</span><span class="o">.</span><span class="n">debug_dump</span><span class="p">(</span>
                                    <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">DEBUG_LOGGING_DIR</span><span class="si">}</span><span class="s2">/</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">name</span><span class="si">}</span><span class="s2">_cudagraph.dot&quot;</span>
                                <span class="p">)</span>

                        <span class="bp">self</span><span class="o">.</span><span class="n">cudagraph</span><span class="o">.</span><span class="n">replay</span><span class="p">()</span>  <span class="c1"># type: ignore</span>

                    <span class="k">else</span><span class="p">:</span>
                        <span class="bp">self</span><span class="o">.</span><span class="n">context</span><span class="o">.</span><span class="n">execute_async_v3</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_engine_stream</span><span class="o">.</span><span class="n">cuda_stream</span><span class="p">)</span>

                <span class="bp">self</span><span class="o">.</span><span class="n">_caller_stream</span><span class="o">.</span><span class="n">wait_stream</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_engine_stream</span><span class="p">)</span>

            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">use_pre_allocated_outputs</span><span class="p">:</span>
                <span class="bp">self</span><span class="o">.</span><span class="n">pre_allocated_outputs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">create_output_tensors</span><span class="p">()</span>

            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">cudagraphs_enabled</span><span class="p">:</span>
                <span class="k">for</span> <span class="n">idx</span><span class="p">,</span> <span class="n">o</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">outputs</span><span class="p">):</span>
                    <span class="n">o</span><span class="o">.</span><span class="n">copy_</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_output_buffers</span><span class="p">[</span><span class="n">idx</span><span class="p">])</span>

            <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">outputs</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
                <span class="k">return</span> <span class="n">outputs</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>

            <span class="k">return</span> <span class="n">outputs</span>

        <span class="k">def</span><span class="w"> </span><span class="nf">run_output_allocator</span><span class="p">()</span> <span class="o">-&gt;</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span> <span class="o">|</span> <span class="n">Tuple</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="o">...</span><span class="p">]:</span>
            <span class="k">assert</span> <span class="p">(</span>
                <span class="ow">not</span> <span class="n">torch_tensorrt</span><span class="o">.</span><span class="n">runtime</span><span class="o">.</span><span class="n">get_cudagraphs_mode</span><span class="p">()</span>
            <span class="p">),</span> <span class="s2">&quot;CUDA Graphs are not compatible with OutputAllocator.&quot;</span>
            <span class="k">with</span> <span class="p">(</span>
                <span class="n">torch</span><span class="o">.</span><span class="n">autograd</span><span class="o">.</span><span class="n">profiler</span><span class="o">.</span><span class="n">record_function</span><span class="p">(</span>
                    <span class="s2">&quot;PythonTorchTensorRTModule:ProcessInputs&quot;</span>
                <span class="p">)</span>
                <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">profiling_enabled</span>
                <span class="k">else</span> <span class="n">nullcontext</span><span class="p">()</span>
            <span class="p">):</span>
                <span class="k">assert</span> <span class="nb">len</span><span class="p">(</span><span class="n">contiguous_inputs</span><span class="p">)</span> <span class="o">==</span> <span class="nb">len</span><span class="p">(</span>
                    <span class="bp">self</span><span class="o">.</span><span class="n">input_names</span>
                <span class="p">),</span> <span class="sa">f</span><span class="s2">&quot;Wrong number of inputs, expect </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">input_names</span><span class="p">)</span><span class="si">}</span><span class="s2"> get </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">contiguous_inputs</span><span class="p">)</span><span class="si">}</span><span class="s2">.&quot;</span>

                <span class="bp">self</span><span class="o">.</span><span class="n">setup_input_tensors</span><span class="p">(</span><span class="n">contiguous_inputs</span><span class="p">,</span> <span class="kc">False</span><span class="p">,</span> <span class="kc">False</span><span class="p">)</span>

            <span class="k">with</span> <span class="p">(</span>
                <span class="n">torch</span><span class="o">.</span><span class="n">autograd</span><span class="o">.</span><span class="n">profiler</span><span class="o">.</span><span class="n">record_function</span><span class="p">(</span>
                    <span class="s2">&quot;PythonTorchTensorRTModule:SetupOutputAllocator&quot;</span>
                <span class="p">)</span>
                <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">profiling_enabled</span>
                <span class="k">else</span> <span class="n">nullcontext</span><span class="p">()</span>
            <span class="p">):</span>
                <span class="bp">self</span><span class="o">.</span><span class="n">create_output_allocator</span><span class="p">()</span>
                <span class="c1"># need to set output allocator every run</span>
                <span class="k">for</span> <span class="n">output_name</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">output_names</span><span class="p">:</span>
                    <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">context</span><span class="o">.</span><span class="n">set_output_allocator</span><span class="p">(</span>
                        <span class="n">output_name</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">output_allocator</span>
                    <span class="p">):</span>
                        <span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span>
                            <span class="sa">f</span><span class="s2">&quot;Failed to set output allocator for </span><span class="si">{</span><span class="n">output_name</span><span class="si">}</span><span class="s2">&quot;</span>
                        <span class="p">)</span>

            <span class="k">with</span> <span class="p">(</span>
                <span class="n">torch</span><span class="o">.</span><span class="n">autograd</span><span class="o">.</span><span class="n">profiler</span><span class="o">.</span><span class="n">record_function</span><span class="p">(</span>
                    <span class="s2">&quot;PythonTorchTensorRTModule:TensorRTRuntime&quot;</span>
                <span class="p">)</span>
                <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">profiling_enabled</span>
                <span class="k">else</span> <span class="n">nullcontext</span><span class="p">()</span>
            <span class="p">):</span>
                <span class="bp">self</span><span class="o">.</span><span class="n">_caller_stream</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">current_stream</span><span class="p">()</span>
                <span class="k">if</span> <span class="p">(</span>
                    <span class="bp">self</span><span class="o">.</span><span class="n">_engine_stream</span> <span class="o">==</span> <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">default_stream</span><span class="p">()</span>
                    <span class="ow">or</span> <span class="bp">self</span><span class="o">.</span><span class="n">_engine_stream</span> <span class="ow">is</span> <span class="kc">None</span>
                <span class="p">):</span>
                    <span class="bp">self</span><span class="o">.</span><span class="n">_engine_stream</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">Stream</span><span class="p">()</span>

                <span class="bp">self</span><span class="o">.</span><span class="n">_engine_stream</span><span class="o">.</span><span class="n">wait_stream</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_caller_stream</span><span class="p">)</span>

                <span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">stream</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_engine_stream</span><span class="p">):</span>
                    <span class="bp">self</span><span class="o">.</span><span class="n">context</span><span class="o">.</span><span class="n">execute_async_v3</span><span class="p">(</span>
                        <span class="bp">self</span><span class="o">.</span><span class="n">_engine_stream</span><span class="o">.</span><span class="n">cuda_stream</span>
                    <span class="p">)</span>  <span class="c1"># The OutputAllocator is called by execute_async_v3()</span>

                <span class="bp">self</span><span class="o">.</span><span class="n">_caller_stream</span><span class="o">.</span><span class="n">wait_stream</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_engine_stream</span><span class="p">)</span>

            <span class="k">with</span> <span class="p">(</span>
                <span class="n">torch</span><span class="o">.</span><span class="n">autograd</span><span class="o">.</span><span class="n">profiler</span><span class="o">.</span><span class="n">record_function</span><span class="p">(</span>
                    <span class="s2">&quot;PythonTorchTensorRTModule:ProcessOutputs&quot;</span>
                <span class="p">)</span>
                <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">profiling_enabled</span>
                <span class="k">else</span> <span class="n">nullcontext</span><span class="p">()</span>
            <span class="p">):</span>
                <span class="n">outputs</span> <span class="o">=</span> <span class="p">[]</span>
                <span class="k">assert</span> <span class="bp">self</span><span class="o">.</span><span class="n">output_allocator</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span>
                <span class="k">for</span> <span class="n">o</span><span class="p">,</span> <span class="n">output_name</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">output_names</span><span class="p">):</span>
                    <span class="n">shape</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">output_allocator</span><span class="o">.</span><span class="n">shapes</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">output_name</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
                    <span class="n">dtype</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">output_dtypes</span><span class="p">[</span><span class="n">o</span><span class="p">]</span>
                    <span class="n">output</span> <span class="o">=</span> <span class="p">(</span>
                        <span class="bp">self</span><span class="o">.</span><span class="n">output_allocator</span><span class="o">.</span><span class="n">buffers</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">output_name</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
                        <span class="o">.</span><span class="n">clone</span><span class="p">()</span>
                        <span class="o">.</span><span class="n">detach</span><span class="p">()</span>
                    <span class="p">)</span>
                    <span class="n">prod</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">prod</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">(</span><span class="n">shape</span><span class="p">)))</span>
                    <span class="c1"># When using the OutputAllocator, the allocated buffer might be larger than the size of the output,</span>
                    <span class="c1"># so we need to reshape the buffer to the output shape</span>
                    <span class="n">output</span> <span class="o">=</span> <span class="n">output</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="n">dtype</span><span class="p">)[:</span><span class="n">prod</span><span class="p">]</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">shape</span><span class="p">)</span>
                    <span class="n">outputs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">output</span><span class="p">)</span>

            <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">outputs</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
                <span class="k">return</span> <span class="n">outputs</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>

            <span class="k">return</span> <span class="n">outputs</span>

        <span class="bp">self</span><span class="o">.</span><span class="n">cudagraphs_enabled</span> <span class="o">=</span> <span class="n">torch_tensorrt</span><span class="o">.</span><span class="n">runtime</span><span class="o">.</span><span class="n">get_cudagraphs_mode</span><span class="p">()</span>

        <span class="c1"># Run forward function</span>
        <span class="n">contiguous_inputs</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span>
            <span class="p">(</span><span class="n">i</span><span class="o">.</span><span class="n">contiguous</span><span class="p">()</span> <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">)</span> <span class="k">else</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">(</span><span class="n">i</span><span class="p">)</span><span class="o">.</span><span class="n">cuda</span><span class="p">())</span>
            <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">inputs</span>
        <span class="p">]</span>
        <span class="k">with</span> <span class="p">(</span>
            <span class="n">torch</span><span class="o">.</span><span class="n">autograd</span><span class="o">.</span><span class="n">profiler</span><span class="o">.</span><span class="n">record_function</span><span class="p">(</span><span class="s2">&quot;PythonTorchTensorRTModule:Forward&quot;</span><span class="p">)</span>
            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">profiling_enabled</span>
            <span class="k">else</span> <span class="n">nullcontext</span><span class="p">()</span>
        <span class="p">):</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">_check_initialized</span><span class="p">()</span>

            <span class="c1"># If in safe mode, check at each iteration for whether a switch is required</span>
            <span class="k">if</span> <span class="p">(</span>
                <span class="n">torch_tensorrt</span><span class="o">.</span><span class="n">runtime</span><span class="o">.</span><span class="n">_multi_device_safe_mode</span><span class="o">.</span><span class="n">_PY_RT_MULTI_DEVICE_SAFE_MODE</span>
            <span class="p">):</span>
                <span class="n">curr_device_id</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">current_device</span><span class="p">()</span>
                <span class="n">curr_device_properties</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">get_device_properties</span><span class="p">(</span>
                    <span class="n">curr_device_id</span>
                <span class="p">)</span>
                <span class="n">logger</span><span class="o">.</span><span class="n">debug</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Current Device: cuda:</span><span class="si">{</span><span class="n">curr_device_id</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>

                <span class="c1"># If a switch is required, move all inputs to new device and set as active device</span>
                <span class="k">if</span> <span class="n">_is_switch_required</span><span class="p">(</span>
                    <span class="n">curr_device_id</span><span class="p">,</span>
                    <span class="bp">self</span><span class="o">.</span><span class="n">target_device_id</span><span class="p">,</span>
                    <span class="n">curr_device_properties</span><span class="p">,</span>
                    <span class="bp">self</span><span class="o">.</span><span class="n">target_device_properties</span><span class="p">,</span>
                <span class="p">):</span>
                    <span class="n">device_id</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">_select_rt_device</span><span class="p">(</span>
                        <span class="n">curr_device_id</span><span class="p">,</span>
                        <span class="bp">self</span><span class="o">.</span><span class="n">target_device_id</span><span class="p">,</span>
                        <span class="bp">self</span><span class="o">.</span><span class="n">target_device_properties</span><span class="p">,</span>
                    <span class="p">)</span>

                    <span class="c1"># Update current device</span>
                    <span class="n">device</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">device</span><span class="p">(</span><span class="n">device_id</span><span class="p">)</span>
                    <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">set_device</span><span class="p">(</span><span class="n">device_id</span><span class="p">)</span>

                    <span class="n">contiguous_inputs</span> <span class="o">=</span> <span class="p">[</span>
                        <span class="n">tensor</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="p">)</span> <span class="k">for</span> <span class="n">tensor</span> <span class="ow">in</span> <span class="n">contiguous_inputs</span>
                    <span class="p">]</span>
                    <span class="n">logger</span><span class="o">.</span><span class="n">warning</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Moved all input Tensors to cuda:</span><span class="si">{</span><span class="n">device_id</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>

            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">requires_output_allocator</span><span class="p">:</span>  <span class="c1"># engine requires OA</span>
                <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">cudagraphs_enabled</span><span class="p">:</span>
                    <span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span>
                        <span class="s2">&quot;The model contains submodules that require a dynamic output allocator at runtime, which is incompatible with CUDA Graphs. Please disable CUDA Graphs.&quot;</span>
                    <span class="p">)</span>
                <span class="n">logger</span><span class="o">.</span><span class="n">debug</span><span class="p">(</span><span class="s2">&quot;Using the dynamic allocator runtime mode.&quot;</span><span class="p">)</span>
                <span class="k">return</span> <span class="n">run_output_allocator</span><span class="p">()</span>
            <span class="k">else</span><span class="p">:</span>
                <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">use_output_allocator_outputs</span><span class="p">:</span>  <span class="c1"># users call OA context manager</span>
                    <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">cudagraphs_enabled</span><span class="p">:</span>
                        <span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span>
                            <span class="s2">&quot;Both CUDA Graphs and dynamic output allocation are enabled, which are incompatible runtime modes. Please disable one of the two.&quot;</span>
                        <span class="p">)</span>
                    <span class="n">logger</span><span class="o">.</span><span class="n">debug</span><span class="p">(</span><span class="s2">&quot;Using the dynamic allocator runtime mode.&quot;</span><span class="p">)</span>
                    <span class="k">return</span> <span class="n">run_output_allocator</span><span class="p">()</span>
                <span class="k">else</span><span class="p">:</span>
                    <span class="n">logger</span><span class="o">.</span><span class="n">debug</span><span class="p">(</span>
                        <span class="sa">f</span><span class="s2">&quot;Using the standard execution runtime mode with cudagraphs=</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">cudagraphs_enabled</span><span class="si">}</span><span class="s2">.&quot;</span>
                    <span class="p">)</span>
                    <span class="k">return</span> <span class="n">run_standard_execution</span><span class="p">()</span></div>

<div class="viewcode-block" id="PythonTorchTensorRTModule.enable_profiling"><a class="viewcode-back" href="../../../../py_api/runtime.html#torch_tensorrt.runtime.PythonTorchTensorRTModule.enable_profiling">[docs]</a>    <span class="k">def</span><span class="w"> </span><span class="nf">enable_profiling</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">profiler</span><span class="p">:</span> <span class="s2">&quot;trt.IProfiler&quot;</span> <span class="o">=</span> <span class="kc">None</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd">        Enable TensorRT profiling. After calling this function, TensorRT will report</span>
<span class="sd">        time spent on each layer in stdout for each forward run.</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">_check_initialized</span><span class="p">()</span>

        <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">context</span><span class="o">.</span><span class="n">profiler</span><span class="p">:</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">context</span><span class="o">.</span><span class="n">profiler</span> <span class="o">=</span> <span class="n">trt</span><span class="o">.</span><span class="n">Profiler</span><span class="p">()</span> <span class="k">if</span> <span class="n">profiler</span> <span class="ow">is</span> <span class="kc">None</span> <span class="k">else</span> <span class="n">profiler</span>

        <span class="bp">self</span><span class="o">.</span><span class="n">profiling_enabled</span> <span class="o">=</span> <span class="kc">True</span></div>

<div class="viewcode-block" id="PythonTorchTensorRTModule.disable_profiling"><a class="viewcode-back" href="../../../../py_api/runtime.html#torch_tensorrt.runtime.PythonTorchTensorRTModule.disable_profiling">[docs]</a>    <span class="k">def</span><span class="w"> </span><span class="nf">disable_profiling</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd">        Disable TensorRT profiling.</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">_check_initialized</span><span class="p">()</span>
        <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">synchronize</span><span class="p">()</span>
        <span class="k">del</span> <span class="bp">self</span><span class="o">.</span><span class="n">context</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">context</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">engine</span><span class="o">.</span><span class="n">create_execution_context</span><span class="p">()</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">profiling_enabled</span> <span class="o">=</span> <span class="kc">False</span></div>

<div class="viewcode-block" id="PythonTorchTensorRTModule.get_layer_info"><a class="viewcode-back" href="../../../../py_api/runtime.html#torch_tensorrt.runtime.PythonTorchTensorRTModule.get_layer_info">[docs]</a>    <span class="k">def</span><span class="w"> </span><span class="nf">get_layer_info</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">str</span><span class="p">:</span>
<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd">        Get layer info of the engine. Only support for TRT &gt; 8.2.</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="n">inspector</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">engine</span><span class="o">.</span><span class="n">create_engine_inspector</span><span class="p">()</span>
        <span class="n">engine_json</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="n">inspector</span><span class="o">.</span><span class="n">get_engine_information</span><span class="p">(</span>
            <span class="n">trt</span><span class="o">.</span><span class="n">LayerInformationFormat</span><span class="o">.</span><span class="n">JSON</span>
        <span class="p">)</span>
        <span class="k">return</span> <span class="n">engine_json</span></div>

<div class="viewcode-block" id="PythonTorchTensorRTModule.validate_input_shapes"><a class="viewcode-back" href="../../../../py_api/runtime.html#torch_tensorrt.runtime.PythonTorchTensorRTModule.validate_input_shapes">[docs]</a>    <span class="k">def</span><span class="w"> </span><span class="nf">validate_input_shapes</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">inputs</span><span class="p">:</span> <span class="n">Sequence</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">])</span> <span class="o">-&gt;</span> <span class="nb">bool</span><span class="p">:</span>
<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd">        Validates the input shapes of the forward function has changed</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="c1"># Representation of input shapes to a given model</span>
        <span class="c1"># Shapes are concatenated as so:</span>
        <span class="c1"># x: (3, 4), y: (4, 5) --&gt; Key: (3,4)(4,5)</span>
        <span class="n">tensor_inputs</span> <span class="o">=</span> <span class="p">[]</span>
        <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="n">inputs</span><span class="p">:</span>
            <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">t</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">):</span>
                <span class="k">return</span> <span class="kc">True</span>
            <span class="n">tensor_inputs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">t</span><span class="p">)</span>
        <span class="n">new_shape_key</span> <span class="o">=</span> <span class="s2">&quot;&quot;</span><span class="o">.</span><span class="n">join</span><span class="p">(</span>
            <span class="nb">str</span><span class="p">(</span><span class="nb">tuple</span><span class="p">(</span><span class="n">t</span><span class="o">.</span><span class="n">shape</span><span class="p">))</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s2">&quot; &quot;</span><span class="p">,</span> <span class="s2">&quot;&quot;</span><span class="p">)</span> <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="n">tensor_inputs</span>
        <span class="p">)</span>

        <span class="c1"># If the new shape key differs from the existing one,</span>
        <span class="c1"># invalidate the old shape key and remove the CUDAGraph</span>
        <span class="k">if</span> <span class="n">new_shape_key</span> <span class="o">!=</span> <span class="bp">self</span><span class="o">.</span><span class="n">shape_key</span><span class="p">:</span>
            <span class="n">logger</span><span class="o">.</span><span class="n">debug</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Input shape changed </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">shape_key</span><span class="si">}</span><span class="s2"> -&gt; </span><span class="si">{</span><span class="n">new_shape_key</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">shape_key</span> <span class="o">=</span> <span class="n">new_shape_key</span>
            <span class="k">return</span> <span class="kc">True</span>

        <span class="k">return</span> <span class="kc">False</span></div></div>
</pre></div>

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